Linda Marrakchi-Kacem

Altered structural connectivity of cortico-striato-pallido-thalamic networks in Gilles de la Tourette syndrome

See Jackson (doi:10.1093/brain/awu338) for a scientific commentary on this article. The neural substrate of Gilles de la Tourette syndrome is unknown. Worbe et al. use probabilistic tractography to demonstrate widespread structural abnormalities in cortico-striato-pallido-thalamic white matter pathways—likely arising from abnormal brain development—in patients with this syndrome.

Detection of volume loss in hippocampal layers in Alzheimer's disease using 7 T MRI: A feasibility study

In Alzheimers disease (AD), the hippocampus is an early site of tau pathology and neurodegeneration. Histological studies have shown that lesions are not uniformly distributed within the hippocampus. Moreover, alterations of different hippocampal layers may reflect distinct pathological processes. 7 T MRI dramatically improves the visualization of hippocampal subregions and layers. In this study, we aimed to assess whether 7 T MRI can detect volumetric changes in hippocampal layers in vivo in patients with AD. We studied four AD patients and seven control subjects. MR images were acquired using a whole-body 7 T scanner with an eight channel transmit–receive coil. Hippocampal subregions were manually segmented from coronal T2*-weighted gradient echo images with 0.3 × 0.3 × 1.2 mm3 resolution using a protocol that distinguishes between layers richer or poorer in neuronal bodies. Five subregions were segmented in the region of the hippocampal body: alveus, strata radiatum, lacunosum and moleculare (SRLM) of the cornu Ammonis (CA), hilum, stratum pyramidale of CA and stratum pyramidale of the subiculum. We found strong bilateral reductions in the SRLM of the cornu Ammonis and in the stratum pyramidale of the subiculum (p < 0.05), with average cross-sectional area reductions ranging from −29% to −49%. These results show that it is possible to detect volume loss in distinct hippocampal layers using segmentation of 7 T MRI. 7 T MRI-based segmentation is a promising tool for AD research.

Biomedical image segmentation using geometric deformable models and metaheuristics

This paper describes a hybrid level set approach for medical image segmentation. This new geometric deformable model combines region- and edge-based information with the prior shape knowledge introduced using deformable registration. Our proposal consists of two phases: training and test. The former implies the learning of the level set parameters by means of a Genetic Algorithm, while the latter is the proper segmentation, where another metaheuristic, in this case Scatter Search, derives the shape prior. In an experimental comparison, this approach has shown a better performance than a number of state-of-the-art methods when segmenting anatomical structures from different biomedical image modalities.

Genetic algorithms for Voxel-based medical image registration

Image registration (IR) - the task of aligning different images having a common content - is a fundamental problem in computer vision. In particular, IR is one of the key steps in medical imaging, with applications ranging from computer assisted diagnosis to computer aided therapy and surgery. As IR can be formulated as an optimization problem, a large family of metaheuristics methods can be used to improve the results obtained by classic gradient-based, continuous optimization techniques. In this work, we extend our previous intensity-based image registration (IR) technique based on a real-coded genetic algorithm with a more appropriate design. The performance evaluation of an heterogeneous group of state-of-the-art IR techniques is also extended to two experimental studies on both synthetic and real-word medical IR problems. The results prove the accuracy and applicability of our new method.

Multi-contrast deep nuclei segmentation using a probabilistic atlas

In this paper we propose a new hybrid segmentation approach of the deep brain structures based on a multi-contrast deformable model of regions in competition, with deformations preserving the topology of the structures, as well as their shape and position, using a probabilistic atlas and some prior morphological information. The accuracy of our method was evaluated by comparing the results obtained on a base of T1-weighted data contrast with those of FREESURFER and FSL-FIRST. Besides giving very good results from only one contrast, we show that the multi-contrast aspect of our method allows exploiting the complementary contributions of different contrasts, like T1 and diffusion tensor (DT) contrasts, in order to provide a more robust segmentation.

Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles

Fiber bundles stemming from tractography algorithms contain many streamlines. They require therefore a great amount of computer memory and computational resources to be stored, visualised and processed. We propose an approximation scheme for fiber bundles which results in a parsimonious representation of weighted prototypes. Prototypes are chosen among the streamlines and they represent groups of similar streamlines. Their weight is related to the number of approximated streamlines. Both streamlines and prototypes are modelled as weighted currents. This computational model does not need point-to-point correspondences and two streamlines are considered similar if their endpoints are close to each other and if their pathways follow similar trajectories. Moreover, the space of weighted currents is a vector space with a closed-form metric. This permits easy computation of the approximation error and the selection of the prototypes is based on the minimisation of this error. We propose an iterative algorithm which approximates independently and simultaneously all the fascicles of the bundle in a fast and accurate way. We show that the resulting representation preserves the shape of the bundle and it can be used to accurately reconstruct the original structural connectivity. We evaluate our algorithm on bundles obtained from both deterministic and probabilistic tractography algorithms. The resulting approximations use on average only 2% of the original streamlines as prototypes. This drastically reduces the computational burden of the processes where the geometry of the streamlines is considered. We demonstrate its effectiveness using as example the registration between two fiber bundles.

Analysis of the striato-thalamo-cortical connectivity on the cortical surface to infer biomarkers of huntington's disease

The deep brain nuclei play an important role in many brain functions and particularly motor control. Damage to these structures result in movement disorders such as in Parkinsons disease or Huntingtons disease, or behavioural disorders such as Tourette syndrome. In this paper, we propose to study the connectivity profile of the deep nuclei to the motor, associative or limbic areas and we introduce a novel tool to build a probabilistic atlas of these connections to the cortex directly on the surface of the cortical mantel, as it corresponds to the space of functional interest. The tool is then applied on two populations of healthy volunteers and patients suffering from severe Huntingtons disease to produce two surface atlases of the connectivity of the basal ganglia to the cortical areas. Finally, robust statistics are used to characterize the differences of that connectivity between the two populations, providing new connectivity-based biomarkers of the pathology.

Evolutionary medical image registration using automatic parameter tuning

Image registration is a fundamental step in combining information from multiple images in medical imaging, computer vision and image processing. In this paper, we configure a recent evolutionary algorithm for medical image registration, r-GA, with an offline automatic parameter tuning technique. In addition, we demonstrate the use of automatic tuning to compare different registration algorithms, since it allows to consider results that are not affected by the ability and efforts invested by the designers in configuring the different algorithms, a crucial task that strongly impacts their performance. Our experimental study is carried out on a large dataset of brain MRI, on which we compare the performance of r-GA with four classic IR techniques. Our results show that all algorithms benefit from the automatic tuning process and indicate that r-GA performs significantly better than the competitors.

A prototype representation to approximate white matter bundles with weighted currents.

Quantitative and qualitative analysis of white matter fibers resulting from tractography algorithms is made difficult by their huge number. To this end, we propose an approximation scheme which gives as result a more concise but at the same time exhaustive representation of a fiber bundle. It is based on a novel computational model for fibers, called weighted currents, characterised by a metric that considers both the pathway and the anatomical locations of the endpoints of the fibers. Similarity has therefore a twofold connotation: geometrical and related to the connectivity. The core idea is to use this metric for approximating a fiber bundle with a set of weighted prototypes, chosen among the fibers, which represent ensembles of similar fibers. The weights are related to the fibers represented b y t he prototypes. The algorithm is divided into two steps. First, the main modes of the fiber bundle are detected using a modularity based clustering algorithm. Second, a prototype fiber selection process is carried on in each cluster separately. This permits to explain the main patterns of the fiber bundle in a fast and accurate way.

Automatic evolutionary medical image segmentation using deformable models

This paper describes a hybrid level set approach to medical image segmentation. The method combines region-and edge-based information with the prior shape knowledge introduced using deformable registration. A parameter tuning mechanism, based on Genetic Algorithms, provides the ability to automatically adapt the level set to different segmentation tasks. Provided with a set of examples, the GA learns the correct weights for each image feature used in the segmentation. The algorithm has been tested over four different medical datasets across three image modalities. Our approach has shown significantly more accurate results in comparison with six state-of-the-art segmentation methods. The contributions of both the image registration and the parameter learning steps to the overall performance of the method have also been analyzed.